# User-Centric Semi-Automated Infographics Authoring and Recommendation

arXiv: https://arxiv.org/abs/2108.11914 · HTML: https://mlsanity.com/paper/2108.11914/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 68 references that are in the map.
- First posted on arXiv 26 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Data2Vis: Automatic Generation of Data Visualizations Using Sequence to Sequence Recurrent Neural Networks](https://mlsanity.com/paper/1804.03126/) (2018)
- [VizML: A Machine Learning Approach to Visualization Recommendation](https://mlsanity.com/paper/1808.04819/) (2018)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [Predicting Visual Importance Across Graphic Design Types](https://mlsanity.com/paper/2008.02912/) (2020)
- [Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline](https://mlsanity.com/paper/1907.13550/) (2019)
- [Synthetically Trained Icon Proposals for Parsing and Summarizing Infographics](https://mlsanity.com/paper/1807.10441/) (2018)
- [Task Classification Model for Visual Fixation, Exploration, and Search](https://mlsanity.com/paper/1907.12635/) (2019)
- [Understanding Infographics through Textual and Visual Tag Prediction](https://mlsanity.com/paper/1709.09215/) (2017)

## Built on by (most cited first, 2 of 2)

- [LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models](https://mlsanity.com/paper/2303.02927/) (2023)
- [Infographics Wizard: Flexible Infographics Authoring and Design Exploration](https://mlsanity.com/paper/2204.09904/) (2022)

## Abstract (arXiv)

Designing infographics can be a tedious process for non-experts and time-consuming even for professional designers. Based on the literature and a formative study, we propose a flexible framework for automated and semi-automated infographics design. This framework captures the main design components in infographics and streamlines the generation workflow into three steps, allowing users to control and optimize each aspect independently. Based on the framework, we also propose an interactive tool, name, for assisting novice designers with creating high-quality infographics from an input in a markdown format by offering recommendations of different design components of infographics. Simultaneously, more experienced designers can provide custom designs and layout ideas to the tool using a canvas to control the automated generation process partially. As part of our work, we also contribute an individual visual group (VG) and connection designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs. This dataset plays a crucial role in diversifying the infographic designs created by our framework. We evaluate our approach with a comparison against similar tools, a user study with novice and expert designers, and a case study. Results confirm that our framework and name excel in creating customized infographics and exploring a large variety of designs.